A space‐time downscaling model for rainfall

Abstract

Abstract.Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical downscaling, the latter being especially attractive in ensemble predictions due to its computational efficiency. Most existing precipitation downscaling schemes are based on spatial disaggregation of rainfall patterns, independently at different times, and do not properly account for the temporal persistence of rainfall at the subgrid spatial scales. Such a temporal persistence in rainfall directly relates to the spatial variability of accumulated local soil moisture and might be important if the downscaled values were to be used in a coupled atmospheric-hydrologic model. In this paper we propose a rainfall downscaling model which utilizes the presence of dynamic scaling in rainfall [Venugopal et al., 1999] and which in conjunction with a spatial disaggregation scheme preserves both the temporal and spatial correlation structure of rainfall at the subgrid scales.

Advances in the use of observed spatial patterns of catchment hydrological response

“…More generally, we may draw some confidence for the future from the work done in precipitation where the availability of detailed spatial observations (from radar) has enabled new methods for generating space-time patterns of rainfall that preserve the key features of these intricate patterns (e.g. [90,99]). As more data becomes available on other variables of hydrological interest, we anticipate similar advances to occur.…”

Towards a definition of a real-time forecasting network for rainfall induced shallow landslides

“…However, linking these models with the features of the large-scale fields is not easy. Many downscaling procedures, currently available for operational purposes, account only for the total precipitation predicted by the LAM, while some other models are based on CAPE (Convective Available Potential Energy) predictions (Perica and Foufoula-Georgiou, 1996;Venugopal et al, 1999), while other information provided by the meteorological model are not preserved.…”

Compressed sensing applied to weather radar

“…A related application is downscaling (Venugopal et al 1999), where the sparsity of precipitation image in wavelet domain is used to form a high-resolution precipitation image from low-resolution measurements (Ebtehaj et al 2012). …”